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Functional groups revisited Steve Pennings University of Houston 1 Groups versus continuous • Groups easy, natural, but • There may be overlap within groups • Continuous data better for models/statistics 2 Some groups may be real • Herbivore feeding groups: chewing versus sucking • C3, C4 and CAM photosynthesis • Web-building, ambush, hunting spiders 3 Trophic levels Examination of food webs indicates that trophic levels 0 (plants) and 1 (herbivores) are discrete, but all higher levels are continuous. Thompson et al. 2007, Ecology 88:612-617. Trophic level 4 Practical issue • “Soft” or categorical traits are often much easier to get. • Fertilization synthesis group used categories (N-fixing or not, C3 vs. C4, short vs. tall, etc.) because we couldn’t get continuous traits for a large number of species. • We are now talking about measuring one or two continuous traits. 5 Three approaches • 1) Concept-driven • 2) Natural groupings • 3) Testing relationships 6 1) Concept-driven • Based on theory/model, measure traits that should matter 7 From P. Grime 1979 8 Plant Apparency Theory--Paul Feeny 9 Body size James Brown and colleagues 10 2) Natural groupings • Natural groupings can be identified by “obvious differences”, tree-based methods, or correlation-based methods. • It is reasonable to expect traits to correlate with each other if there are trade-offs. • It is reasonable to expect traits to fall into discrete groups if only some combinations of traits function effectively. 11 Leaf traits Photosynthesis on a per gram basis is correlated with leaf mass per unit area (LMA) and leaf nitrogen (per gram). 2,548 species and 175 sites. Single “leaf economic spectrum” running from quick return (high nutrient and A, short leaf life) to slow return (low nutrient and A, long leaf life) on investments in leaves. Functional groups show substantial overlap along the spectrum. Wright et al. 2004 Nature 428:821827. Because many leaf traits are correlated with each other, you don’t need to measure them all. 12 Leaf traits and plant functional groups • Leaf traits differ on average among common plant functional groups (deciduous, evergreen, herb, etc.), but there is a lot of overlap. Continuous index is better than groups. • Continuous variables provide better link to ecosystems ecology because currencies are leaf nutrients and turnover times. • Reich et al. 1992, Ecol Mongr 62:365-392 • Reich et al. 1997, PNAS 94:13730-13734 13 Plant traits using cluster analysis • Boutin and Keddy 1993 Journal of Vegetation Science 4:591-600 • 27 traits on 43 species of wetland plants • cluster analysis to group plants 14 15 Herbivore feeding guilds • • • • • Chewing Sucking Mining Galling Boring 16 Caterpillar feeding guilds • Caterpillar families form guilds • Saturniids: cut leaves into large pieces, short simple mandibles, eat old, tough leaves with tannins • Sphingids: tear and crush leaves into small pieces, long, toothed mandibles, eat young, soft leaves with toxins • Bernays and Janzen 1988, Ecology 69:1153-1160 17 Predatory fish feeding guilds • • • • Suction (bass, grouper) Ram (filter feeder) Bite (great white shark) Because these methods require fundamentally different musculature, they probably are discrete categories 18 Algal functional groups Steneck and Watling 1982, Marine Biology 65:299-319, proposed that algae fall into seven functional groups that were increasingly harder for herbivores to graze. Argued for a match between algal group and herbivore feeding mode leading to specialization. 1) Microalgae like diatoms 2) Filamentous algae 3) Foliose algae with thin blades like Ulva 4) Corticated algae with fine branching structure 5) Leathery algae with thick, tough blades, like kelps 6) Articulated calcareous algae 7) Crustose coralline algae 19 Danger • Natural groups may not be relevant to your question • Humans: height and mass correlated, differ among groups (sex). • But diabetes predicted by mass:height ratio, not their positive relationship and not by sex. 20 3) Testing relationships • Solution: test whether traits explain natural processes? • Effects and responses • Testing can both refine existing classification schemes and suggest new approaches in an iterative fashion 21 Effect: Population traits Leaf traits are not only correlated with each other, but also predict population-level traits such as growth rate and survival. At a single site, traits varied 3-15 fold among species (53 rain forest tree species in Bolivia). Species with short-lived high A leaves had high growth rates but low survival. Poorter and Bongers 2006. Ecology 87:1733-1743. 22 Effect: Competition Competitive ability (competitive effect on a common “phytometer”) in two pot sizes was correlated, and could be predicted based on traits related to plant size and leaf shape (Keddy et al. 2002, J. Veg. Sci. 13:5-16). Earlier study with wetland plants also found that competitive effect was related to size and leaf shape (Gaudet and Keddy 1988, Nature 334:242-243). 23 Effect: Diversity • Many studies of diversity use “natural” groups of grass, forb, legume • Some also divide grasses into C3 and C4 • Wright et al. 2006, Ecology letters 9:111120 tested whether natural groups better than random in 10 experiments from the literature. 24 Best post-hoc group Natural group In many cases, natural groups were no better than average random groups. Best post-hoc groups differed among experiments and variables. 25 Effect: Predation Effects of six aquatic predators (fish and salamanders) on larval amphibians could be predicted by traits, but different traits predicted effects on different species. Overall, similarity among predators in traits did not predict similarity in effects. In order to use traits to predict effects, you need to know which traits matter for which effect. 26 Response: plant stress • Koricheva et al. 1998, Annu Rev Ecol Syst 43:195-216, reviewed studies on how plant stress affected herbivores • Stress benefitted boring and sucking insects • Stress negatively affected galling and chewing insects 27 Response: Climate Photosynthetic rate (A) is negatively correlated with leaf mass per unit area (LMA) and positively correlated with leaf nitrogen content (N). A is higher at drier (left graph) and hotter (right graph) sites. 18% of variation in traits was driven by climate. Wright et al. 2005. Global Ecology and Biogeography 14:411-421. 28 Response: N deposition The solid line is the normal relationship. Some species lie above this line and some below it. Atmospheric deposition should favor species like a or b that occur at a relatively high level of soil N for a given pH (or can tolerate acidification at a given N level). These have a positive Ndev value. 29 Response: N deposition An index of local soil nitrogen availability (Ndev) for each species predicted its long-term response to increasing nitrogen availability in Sweden (positive values mean soil N is higher than expected based on soil pH). Other traits (height, growth rate, leaf N, phenology, etc.) also predicted long-term trends, but not as well. Diekmann and Falkengren-Grerup 2002. J Ecol 90:108-120. 30 In a seasonal forest in Panama, dry season leaves (open circles) had more mass per unit area, higher maximum photosynthetic rates (Amax) per unit area and higher water-use efficiency than wet season leaves (closed circles). Kitajima et al. 1997. Oecologia 109:490-498. Amax per unit area Response: seasonality Stomatal Conductance 31 Phenotypic plasticity Diaz and Cabido 2001 32 Leaf area consumed (mm2) Stress gradients Salt marshes have strong gradients in salinity and waterlogging, which affect plant height and other traits. Example: plants from saltier areas of the marsh were more palatable to herbivores than conspecifics from less-salty areas of the marsh. Goranson et al. 2004, Oecologia 140:591-600. 33 Latitudinal gradients 1 24 22 20 Leaf area (cm2) Leaves of Iva frutescens are 4 times larger at high than low latitudes. Pennings unpublished. 18 16 14 12 10 8 6 4 2 30 32 34 36 38 40 42 44 Latitude 34 Latitudinal gradients 2 Pennings et al. 2001, Ecology 85:1344-1359 35 Latitudinal gradients 3 In a common garden study, leaf N and C of St. John’s Wort varied across latitude in plants from both native and introduced populations. Maron et al. 2007. Evolution 61:19121924. For more on latitudinal variation in leaf N and P, see Reich and Oleksyn 2004, PNAS 101:11001-11006. 36 Lessons learned • Continuous traits better than categories • But easier to get data for categories • Different traits may matter for different processes • Be careful about intraspecific variation if environment is variable • Further reading: Petchey and Gaston 2006, Ecology Letters 9:741-758 37